79 structural-engineering "https:" "https:" "https:" "https:" "UCL" "UCL" Postdoctoral positions at University of Oxford
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We are seeking a full-time Postdoctoral Research Associate (PDRA) to join the Environmental and Biological Systems Engineering research group at the Department of Engineering Science (central Oxford
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2025). Our microchip-based escape-time technology platform now enables measurements of the physical properties of macromolecules such as their electrical charge, size and 3D conformation with
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band structure and exciton binding energies, as well as their vibrational and transport properties. This role will utilize several state-of-the-art computational modelling techniques, in particular
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We are seeking a full-time Postdoctoral Research Assistant to join the Environmental Fluid Mechanics Group at the Department of Engineering Science in central Oxford. The post is externally funded
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years This role will contribute directly to drug discovery efforts through the design and synthesis of small-molecule inhibitors. The postholder will use structural, biochemical and microbiological data
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design, development and execution of biochemical and biophysical assays to identify and evaluate inhibitors targeting mycobacterial proteins. Working closely with chemists, structural biologists
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science, engineering, or a related discipline, with significant postdoctoral research experience. The ideal candidate will have strong expertise in computational biology, machine learning, and quantitative analysis
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. The group investigates both the fundamental properties of these proteins and their applications in biotechnology. A long-standing focus of the laboratory is the engineering of protein nanopores
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the molecular structural dynamics and embedded chiral information in redox-active polycyclic aromatic hydrocarbons influence the emergence of novel photoluminescent properties. While the majority of objectives
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to work within one of, or across, the four research themes: Learning with Structured & Geometric Models, Low Effective-dimensional Learning Models, Implicit Regularization, and Reinforcement Learning